Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.

Colonoscopy is central to colorectal cancer (CRC) prevention, and its effectiveness is determined by the quality of mucosal inspection and lesion detection. The adenoma detection rate (ADR) remains the most widely validated quality benchmark due to its strong inverse association with interval CRC. However, reliance on ADR alone is increasingly recognized as insufficient, particularly given the growing understanding of the serrated neoplasia pathway, which contributes up to one-third of sporadic CRCs. This has driven the emergence of complementary metrics, such as the sessile polyp detection rate (SPDR) and adenomas per colonoscopy (APC). Although SPDR offers important advantages for capturing serrated pathology, challenges persist, including interobserver variability, inconsistent pathology thresholds, limited endoscopist training, and the absence of standardized benchmarks. Alongside these evolving metrics, technological advancements such as image-enhanced endoscopy, computer-aided detection, high-definition optics, and distal attachment devices have demonstrated measurable improvements in detecting subtle lesions and reducing operator-dependent variability. Large real-world registries, including GIQuIC, now support the development and validation of composite models that integrate ADR, SPDR, and APC to better reflect the full spectrum of neoplasia detection. As the field advances, redefining colonoscopy quality will require reconciling established metrics with newer indicators that more comprehensively address both conventional adenomas and serrated lesions.

Background: Eosinophilic esophagitis (EoE) is a chronic immune-mediated esophageal disease characterized by esophageal dysfunction and eosinophilic mucosal inflammation. Although epithelial barrier dysfunction, microbial dysbiosis, and environmental exposures have been implicated in disease pathogenesis, occupational contributors remain poorly understood. We aimed to describe a case series of physicians diagnosed with EoE and explore potential shared occupational factors that may contribute to disease development. Case Description: We identified five male physicians aged 25–41 years diagnosed with biopsy-confirmed EoE at a tertiary academic referral center in the northeastern United States. The cohort included one hospitalist, one surgical resident, one family medicine attending physician, and two internal medicine resident physicians. All presented with 1–3 years of intermittent solid-food dysphagia and episodic food impaction. Select patients had atopic comorbidities including asthma, eczema, or seafood allergy. Endoscopic findings demonstrated classic inflammatory EoE features, including edema, linear furrowing, exudates, and mucosal fragility. One patient required endoscopic stricture dilation. Initial treatment included proton pump inhibitor therapy in four patients and swallowed topical corticosteroids in one patient, with adjunct dietary elimination in one case. All patients demonstrated clinical improvement or complete symptom resolution and remained clinically stable at 6-month follow-up. Conclusions: We report a unique cluster of EoE among physicians, highlighting a previously unexplored potential occupational association. While these findings are hypothesis-generating and do not establish causality, they support further investigation into whether healthcare-associated microbial, environmental, and circadian exposures contribute to EoE pathogenesis.